OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.

A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

A collection of resources on applications of Transformers in Medical Imaging.

A contrastive learning based semi-supervised segmentation network for medical image segmentation

BCDU-Net : Medical Image Segmentation

Official Pytorch Code base for "UNeXt: MLP-based Rapid Medical Image Segmentation Network", MICCAI 2022

PraNet: Parallel Reverse Attention Network for Polyp Segmentation, MICCAI 2020 (Oral). Code using Jittor Framework is available.

Context Axial Reverse Attention Network for Small Medical Objects Segmentation

[MICCAI 2023] MedNeXt is a fully ConvNeXt architecture for 3D medical image segmentation.

This repo provides the official code for : 1) TransBTS: Multimodal Brain Tumor Segmentation Using Transformer (https://arxiv.org/abs/2103.04430) , accepted by MICCAI2021. 2) TransBTSV2: Towards Better and More Efficient Volumetric Segmentation of Medical Images(https://arxiv.org/abs/2201.12785).

A framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning

[ICCVW 2025] SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation

Official Pytorch Code of KiU-Net for Image/3D Segmentation - MICCAI 2020 (Oral), IEEE TMI

Official repo for Medical Image Segmentation Review: The Success of U-Net

We proposed a novel U-Net-based model -- DC-UNet to do medical image segmentation.

The largest pre-trained medical image segmentation model (1.4B parameters) based on the largest public dataset (>100k annotations), up until April 2023.

A PyTorch framework for medical image segmentation

[Patterns (Cell subsidiary)] The official code for "UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation".

CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation